Liu Faqiang, Zheng Hao, Ma Songchen, Zhang Weihao, Liu Xue, Chua Yansong, Shi Luping, Zhao Rong
Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology, Jiaxing 314001, China.
Natl Sci Rev. 2024 Feb 26;11(5):nwae066. doi: 10.1093/nsr/nwae066. eCollection 2024 May.
Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.
受脑启发计算从人类大脑的基本结构和信息处理机制中汲取灵感，近年来获得了显著发展。它已成为一种以脑机双驱动和多网络集成的研究范式。这种范式的一个值得注意的例子是混合神经网络（HNN），它将面向计算机科学的人工神经网络（ANN）与面向神经科学的脉冲神经网络（SNN）集成在一起。HNN在包括感知、认知和学习在内的各种智能任务中展现出明显优势。本文对HNN进行了全面综述，重点介绍了它们的起源、概念、生物学视角、构建框架和支持系统。此外，还提供了关于潜在研究方向的见解和建议，旨在推动HNN范式的发展。
